The goal of this research is to develop the full capability of tandem mass spectrometry for routine organic structure determination. In tandem mass spectrometry, or MS/MS, ions from the source are selected by the first mass analyzer and then undergo fragmenting collision with neutral molecules in a collision region. The ionic products of the collision process are then mass analyzed by a second mass filter and ion detector. The special potential offered by MS/MS for structure determination comes from the individual fragmentation spectrum available for each ion in the normal mass spectrum. Each ion species in the source is related to some substructure of the original molecule and the identity of each of these species can be deduced from its fragmentation spectrum. The direct correlation of ion fragmentation spectra with precursor substructures is only one of many types of structural information available in the overall MS/MS molecular fragmentation map. To make this potentially powerful technique generally useful, it is necessary to extend the knowledge base to known relationships between MS/MS spectral features and substructures, to obtain the molecular structure from the identified substructures, and to automate the entire process from instrument control to final structure output. To accomplish this, we propose to utilize a tandem quadrupole mass spectrometer (TWMS) MS/MS instrument, spectrum and structure databases designed for MS/MS applications, and spectrum matching functions linked to these databases. These existing and available functions will be combined with an intelligent controller and expert systems for matching, generating, and organizing structures. The goal is an automated system for the determination of molecular structure. This system will be used in semi-automatic mode to extend the spectra/substructure knowledge base. In measurement mode, the intelligent controller will direct the measurement process to acquire the data most likely to resolve the remaining structural uncertainties. This project combines the fields of organic mass spectrometry, instrumentation, and artificial intelligence. It is relevant to health-related research because of its potential to determine complex structures (and therefore the complete identity) of previously unknown compounds, detect particular sought substructures in various materials or mixtures, and do so using only micrograms of sample.